class_docstr: Base class for Item-Item Nearest Neighbour recommender models
  here.
  
  Parameters
  ----------
  K : int, optional
      The number of neighbours to include when calculating the item-item
      similarity matrix
  num_threads : int, optional
      The number of threads to use for fitting the model. Specifying 0
      means to default to the number of cores on the machine.
code: def similar_users(self, userid, N=10, filter_users=None, users=None):
      raise NotImplementedError(
          "similar_users isn't implemented for item-item recommenders"
      )
  
imports: import numpy
  from numpy import bincount, log, log1p, sqrt
  from scipy.sparse import coo_matrix, csr_matrix
  from ._nearest_neighbours import NearestNeighboursScorer, all_pairs_knn
  from .recommender_base import RecommenderBase
  from .utils import _batch_call
label_desc: Use image node_id 4 for calling the ItemItemRecommender obj's underlying member method code with example usage: obj.similar_users(userid, N, filter_users, users) without return types